Historically, semiconductor device manufacturers have managed the transition to tighter process/materials specifications by depending on process tool manufacturers to design better and faster process/hardware configurations. As device geometries shrink to the nanometer scale, however, the increasing complexity of manufacturing processes has changed the landscape that must be negotiated to meet and maintain process/materials specifications.
Stand-alone control of process tools (based on equipment state data) will not maintain viable yields at 65 and 45 nm. Advanced device processing requires tool-level control based on the combination of advanced equipment control (AEC) and sensor based process control. Furthermore, tool level control alone cannot meet all of the control needs of advanced device fabrication. System-wide implementation of advanced process control (APC) that integrates AEC with e-diagnostics, Fault Detection and Classification (FDC) and predictive mathematical models of the device manufacturing process are required.
Economic as well as technological drivers exists for the move to AEC/APC. The cost of purchasing (and developing) stand-alone process tools to meet advanced production specifications is expected to be staggering. A cost effective alternative to the purchase of a new generation of process equipment exists through the combined use of add-on sensors with AEC/APC in existing (legacy) equipment. Sensor-based AEC/APC in legacy equipment can drive these tools to the tighter specifications needed for nanometer-scale device fabrication. Additional cost benefits can be realized from reductions in scrap/rework (especially in 300 mm wafer processing) and in lower test wafer use since these can be reduced or even eliminated in systems using wafer- and/or process-state data for process control. Sensor-based AEC/APC can also reduce preventive maintenance downtimes and time to process qualifications, while increasing process capabilities, device performance, yields and fab throughput.
It seems surprising that AEC/APC is not already widely implemented, given such strong driving forces. However, significant obstacles to AEC/APC exist in most fabrication environments. SECS communications links to process tools are typically unique by OEM, slow and have narrow bandwidths unsuited to data from more sophisticated process sensors. Different OEM and add-on sensors provide data on tool and process states at very different bandwidths and at different sampling rates. The synchronization of data capture and data transmission at the system level thus represents a significant hurdle for AEC/APC implementation.
Further, even should these obstacles be overcome, meaningful application of this potential flood of data in process control is a formidable task. Simpler approaches, such as USPC (univariate statistical process control), are well established, but have limitations. USPC is effective in the control of a single response parameter but advanced device fabrication requires control of multiple response variables simultaneously. Both response and independent variables typically have complex interrelationships that USPC can neither evaluate nor control.
Multivariate analysis (MVA) has proven to be an effective tool in process monitoring involving a large number of monitored parameters, and in particular for fault detection. Multivariate analysis detects process changes seen in parameter covariance and correlation. MVA typically requires the construction of a process reference model based on known acceptable working conditions that serve as a reference.
The reference model can be constructed from measured process parameters. In general, the model can be broken down into individual time-dependent models for the individual process steps, and then reconstructed into an upper level model summarizing the overall process.
All subsequent implementations of fault detection for the process compare the same measured parameters in the same fashion (i.e., modeling process steps and summarizing into an upper level model) and determine statistically significant deviations from the known acceptable processes upon which the reference model is based. Effective implementation of MVA depends in large part on the quality of the model.
A need therefore exists for improved systems and methods for detecting and classifying defects associated with manufacturing processes and outputs of the manufacturing processes.